OpenRaiser/Intern-Atlas huggingface dataset:
RITM: Revocation in the Middle
Venue: arXiv cs.CR 2016
Year: 2016

Although TLS is used on a daily basis by many critical applications, the public-key infrastructure that it relies on still lacks an adequate revocation mechanism. An ideal revocation mechanism should be inexpensive, efficient, secure, and privacy-preserving. Moreover, rising trends in pervasive encryption pose new scalability challenges that a modern revocation system should address. In this paper, we investigate how network nodes can deliver certificate-validity information to clients. We present RITM, a framework in which middleboxes (as opposed to clients, servers, or certification authorities) store revocation-related data. RITM provides a secure revocation-checking mechanism that preserves user privacy. We also propose to take advantage of content-delivery networks (CDNs) and argue that they would constitute a fast and cost-effective way to disseminate revocations. Additionally, RITM keeps certification authorities accountable for the revocations that they have issued, and it minimizes overhead at clients and servers, as they have to neither store nor download any messages. We also describe feasible deployment models and present an evaluation of RITM to demonstrate its feasibility and benefits in a real-world deployment.
Detecting "Smart" Spammers On Social Network: A Topic Model Approach
Venue: arXiv cs.CL 2016
Year: 2016

Spammer detection on social network is a challenging problem. The rigid anti-spam rules have resulted in emergence of "smart" spammers. They resemble legitimate users who are difficult to identify. In this paper, we present a novel spammer classification approach based on Latent Dirichlet Allocation(LDA), a topic model. Our approach extracts both the local and the global information of topic distribution patterns, which capture the essence of spamming. Tested on one benchmark dataset and one self-collected dataset, our proposed method outperforms other state-of-the-art methods in terms of averaged F1-score.
Graph Decompositions Analysis and Comparison for Cohesive Subgraphs Detection
Venue: arXiv cs.SI 2016
Year: 2016

Massive networks have shown that the determination of dense subgraphs, where vertices interact a lot, is necessary in order to visualize groups of common interest, and therefore be able to decompose a big graph into smaller structures. Many decompositions have been built over the years as part of research in the graph mining field, and the topic is becoming a trend in the last decade because of the increasing size of social networks and databases. Here, we analyse some of the decompositions methods and also present a novel one, the Vertex Triangle k-core. We then compare them and test them against each other. Moreover, we establish different kind of measures for comparing the accuracy of the decomposition methods. We apply these decompositions to real world graphs, like the Collaboration network of arXiv graph, and found some interesting results.
Mac Williams identities and polarized Riemann-Roch conditions
Venue: arXiv cs.IT 2016
Year: 2016

The present note establishes the equivalence of Mac Williams identities for an additive code C and its dual to Polarized Riemann-Roch Conditions on their Zeta-functions. In such a way, the duality of additive codes appears to be a polarized form of the Serre duality on a smooth irreducible projective curve.
Towards Temporal Graph Databases
Venue: arXiv cs.DB 2016
Year: 2016

In spite of the extensive literature on graph databases (GDBs), temporal GDBs have not received too much attention so far. Temporal GBDs can capture, for example, the evolution of social networks across time, a relevant topic in data analysis nowadays. In this paper we propose a data model and query language (denoted TEG-QL) for temporal GDBs, based on the notion of attribute graphs. This allows a straightforward translation to Neo4J, a well-known GBD. We present extensive examples of the use of TEG-QL, and comment our implementation.
Computer keyboard interaction as an indicator of early Parkinson's disease
Venue: arXiv cs.HC 2016
Year: 2016

Parkinson's disease (PD) is a slowly progressing neurodegenerative disease with early manifestation of motor signs. Objective measurements of motor signs are of vital importance for diagnosing, monitoring and developing disease modifying therapies, particularly for the early stages of the disease when putative neuroprotective treatments could stop neurodegeneration. Current medical practice has limited tools to routinely monitor PD motor signs with enough frequency and without undue burden for patients and the healthcare system. In this paper, we present data indicating that the routine interaction with computer keyboards can be used to detect motor signs in the early stages of PD. We explore a solution that measures the key hold times (the time required to press and release a key) during the normal use of a computer without any change in hardware and converts it to a PD motor index. This is achieved by the automatic discovery of patterns in the time series of key hold times using an ensemble regression algorithm. This new approach discriminated early PD groups from controls with an AUC = 0.81 (n = 42/43; mean age = 59.0/60.1; women = 43%/60%;PD/controls). The performance was comparable or better than two other quantitative motor performance tests used clinically: alternating finger tapping (AUC = 0.75) and single key tapping (AUC = 0.61).
Throughput and range characterization of IEEE 802.11ah
Venue: arXiv cs.NI 2016
Year: 2016

The most essential part of Internet of Things (IoT) infrastructure is the wireless communication system that acts as a bridge for the delivery of data and control messages. However, the existing wireless technologies lack the ability to support a huge amount of data exchange from many battery driven devices spread over a wide area. In order to support the IoT paradigm, the IEEE 802.11 standard committee is in process of introducing a new standard, called IEEE 802.11ah. This is one of the most promising and appealing standards, which aims to bridge the gap between traditional mobile networks and the demands of the IoT. In this paper, we first discuss the main PHY and MAC layer amendments proposed for IEEE 802.11ah. Furthermore, we investigate the operability of IEEE 802.11ah as a backhaul link to connect devices over a long range. Additionally, we compare the aforementioned standard with previous notable IEEE 802.11 amendments (i.e. IEEE 802.11n and IEEE 802.11ac) in terms of throughput (with and without frame aggregation) by utilizing the most robust modulation schemes. The results show an improved performance of IEEE 802.11ah (in terms of power received at long range while experiencing different packet error rates) as compared to previous IEEE 802.11 standards.
Universal Indexes for Highly Repetitive Document Collections
Venue: arXiv cs.IR 2016
Year: 2016

Indexing highly repetitive collections has become a relevant problem with the emergence of large repositories of versioned documents, among other applications. These collections may reach huge sizes, but are formed mostly of documents that are near-copies of others. Traditional techniques for indexing these collections fail to properly exploit their regularities in order to reduce space. We introduce new techniques for compressing inverted indexes that exploit this near-copy regularity. They are based on run-length, Lempel-Ziv, or grammar compression of the differential inverted lists, instead of the usual practice of gap-encoding them. We show that, in this highly repetitive setting, our compression methods significantly reduce the space obtained with classical techniques, at the price of moderate slowdowns. Moreover, our best methods are universal, that is, they do not need to know the versioning structure of the collection, nor that a clear versioning structure even exists. We also introduce compressed self-indexes in the comparison. These are designed for general strings (not only natural language texts) and represent the text collection plus the index structure (not an inverted index) in integrated form. We show that these techniques can compress much further, using a small fraction of the space required by our new inverted indexes. Yet, they are orders of magnitude slower.
User Selection and Power Allocation in Full Duplex Multi-Cell Networks
Venue: arXiv cs.NI 2016
Year: 2016

Full duplex (FD) communications has the potential to double the capacity of a half duplex (HD) system at the link level. However, in a cellular network, FD operation is not a straightforward extension of half duplex operations. The increased interference due to a large number of simultaneous transmissions in FD operation and realtime traffic conditions limits the capacity improvement. Realizing the potential of FD requires careful coordination of resource allocation among the cells as well as within the cell. In this paper, we propose a distributed resource allocation, i.e., joint user selection and power allocation for a FD multi-cell system, assuming FD base stations (BSs) and HD user equipment (UEs). Due to the complexity of finding the globally optimum solution, a sub-optimal solution for UE selection, and a novel geometric programming based solution for power allocation, are proposed. The proposed distributed approach converges quickly and performs almost as well as a centralized solution, but with much lower signaling overhead. It provides a hybrid scheduling policy which allows FD operations whenever it is advantageous, but otherwise defaults to HD operation. We focus on small cell systems because they are more suitable for FD operation, given practical self-interference cancellation limits.With practical self-interference cancellation, it is shown that the proposed hybrid FD system achieves nearly two times throughput improvement for an indoor multi-cell scenario, and about 65% improvement for an outdoor multi-cell scenario compared to the HD system.
Distributed Cell Association for Energy Harvesting IoT Devices in Dense Small Cell Networks: A Mean-Field Multi-Armed Bandit Approach
Venue: arXiv cs.NI 2016
Year: 2016

The emerging Internet of Things (IoT)-driven ultra-dense small cell networks (UD-SCNs) will need to combat a variety of challenges. On one hand, massive number of devices sharing the limited wireless resources will render centralized control mechanisms infeasible due to the excessive cost of information acquisition and computations. On the other hand, to reduce energy consumption from fixed power grid and/or battery, many IoT devices may need to depend on the energy harvested from the ambient environment (e.g., from RF transmissions, environmental sources). However, due to the opportunistic nature of energy harvesting, this will introduce uncertainty in the network operation. In this article, we study the distributed cell association problem for energy harvesting IoT devices in UD-SCNs. After reviewing the state-of-the-art research on the cell association problem in small cell networks, we outline the major challenges for distributed cell association in IoT-driven UD-SCNs where the IoT devices will need to perform cell association in a distributed manner in presence of uncertainty (e.g., limited knowledge on channel/network) and limited computational capabilities. To this end, we propose an approach based on mean-field multi-armed bandit games to solve the uplink cell association problem for energy harvesting IoT devices in a UD-SCN. This approach is particularly suitable to analyze large multi-agent systems under uncertainty and lack of information. We provide some theoretical results as well as preliminary performance evaluation results for the proposed approach.
Discriminators and k-Regular Sequences
Venue: arXiv cs.DM 2016
Year: 2016

The discriminator of an integer sequence s = (s(i))_{i >=0}, introduced by Arnold, Benkoski, and McCabe in 1985, is the map D_s(n) that sends n >= 1 to the least positive integer m such that the n numbers s(0), s(1), ..., s(n-1) are pairwise incongruent modulo m. In this note we consider the discriminators of a certain class of sequences, the k-regular sequences. We compute the discriminators of two such sequences, the so-called "evil" and "odious" numbers, and show they are 2-regular. We also give an example of a k-regular sequence whose discriminator is not k-regular. Finally, we examine sequences that are their own discriminators, and count the number of length-$n$ finite sequences with this property.
Application of artificial neural networks and genetic algorithms for crude fractional distillation process modeling
Venue: arXiv cs.NE 2016
Year: 2016

This work presents the application of the artificial neural networks, trained and structurally optimized by genetic algorithms, for modeling of crude distillation process at PKN ORLEN S.A. refinery. Models for the main fractionator distillation column products were developed using historical data. Quality of the fractions were predicted based on several chosen process variables. The performance of the model was validated using test data. Neural networks used in companion with genetic algorithms proved that they can accurately predict fractions quality shifts, reproducing the results of the standard laboratory analysis. Simple knowledge extraction method from neural network model built was also performed. Genetic algorithms can be successfully utilized in efficient training of large neural networks and finding their optimal structures.
MIMO Precoding for Networked Control Systems with Energy Harvesting Sensors
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, we consider a MIMO networked control system with an energy harvesting sensor, where an unstable MIMO dynamic system is connected to a controller via a MIMO fading channel. We focus on the energy harvesting and MIMO precoding design at the sensor so as to stabilize the unstable MIMO dynamic plant subject to the energy availability constraint at the sensor. Using the Lyapunov optimization approach, we propose a closed-form dynamic energy harvesting and dynamic MIMO precoding solution, which has an event-driven control structure. Furthermore, the MIMO precoding solution is shown to have an eigenvalue water-filling structure, where the water level depends on the state estimation covariance, energy queue and the channel state, and the sea bed level depends on the state estimation covariance. The proposed scheme is also compared with various baselines and we show that significant performance gains can be achieved.
Gains of Restricted Secondary Licensing in Millimeter Wave Cellular Systems
Venue: arXiv cs.IT 2016
Year: 2016

Sharing the spectrum among multiple operators seems promising in millimeter wave (mmWave) systems. One explanation is the highly directional transmission in mmWave, which reduces the interference caused by one network on the other networks sharing the same resources. In this paper, we model a mmWave cellular system where an operator that primarily owns an exclusive-use license of a certain band can sell a restricted secondary license of the same band to another operator. This secondary network has a restriction on the maximum interference it can cause to the original network. Using stochastic geometry, we derive expressions for the coverage and rate of both networks, and establish the feasibility of secondary licensing in licensed mmWave bands. To explain economic trade-offs, we consider a revenue-pricing model for both operators in the presence of a central licensing authority. Our results show that the original operator and central network authority can benefit from secondary licensing when the maximum interference threshold is properly adjusted. This means that the original operator and central licensing authority have an incentive to permit a secondary network to restrictively share the spectrum. Our results also illustrate that the spectrum sharing gains increase with narrow beams and when the network densifies.
Information-Theoretical Analysis of Two Shannon's Ciphers
Venue: arXiv cs.CR 2016
Year: 2016

We describe generalized running key ciphers and apply them for analysis of two Shannon's methods. In particular, we suggest some estimation of the cipher equivocation and the probability of correct deciphering without key.
Text-mining the NeuroSynth corpus using Deep Boltzmann Machines
Venue: arXiv cs.LG 2016
Year: 2016

Large-scale automated meta-analysis of neuroimaging data has recently established itself as an important tool in advancing our understanding of human brain function. This research has been pioneered by NeuroSynth, a database collecting both brain activation coordinates and associated text across a large cohort of neuroimaging research papers. One of the fundamental aspects of such meta-analysis is text-mining. To date, word counts and more sophisticated methods such as Latent Dirichlet Allocation have been proposed. In this work we present an unsupervised study of the NeuroSynth text corpus using Deep Boltzmann Machines (DBMs). The use of DBMs yields several advantages over the aforementioned methods, principal among which is the fact that it yields both word and document embeddings in a high-dimensional vector space. Such embeddings serve to facilitate the use of traditional machine learning techniques on the text corpus. The proposed DBM model is shown to learn embeddings with a clear semantic structure.
Dominant Codewords Selection with Topic Model for Action Recognition
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we propose a framework for recognizing human activities that uses only in-topic dominant codewords and a mixture of intertopic vectors. Latent Dirichlet allocation (LDA) is used to develop approximations of human motion primitives; these are mid-level representations, and they adaptively integrate dominant vectors when classifying human activities. In LDA topic modeling, action videos (documents) are represented by a bag-of-words (input from a dictionary), and these are based on improved dense trajectories. The output topics correspond to human motion primitives, such as finger moving or subtle leg motion. We eliminate the impurities, such as missed tracking or changing light conditions, in each motion primitive. The assembled vector of motion primitives is an improved representation of the action. We demonstrate our method on four different datasets.
Morphological and Embedded Computation in a Self-contained Soft Robotic Hand
Venue: arXiv cs.RO 2016
Year: 2016

We present a self-contained, soft robotic hand composed of soft pneumatic actuator modules that are equipped with strain and pressure sensing. We show how this data can be used to discern whether a grasp was successful. Co-locating sensing and embedded computation with the actuators greatly simplifies control and system integration. Equipped with a small pump, the hand is self-contained and needs only power and data supplied by a single USB connection to a PC. We demonstrate its function by grasping a variety of objects ranging from very small to large and heavy objects weighing more than the hand itself. The presented system nicely illustrates the advantages of soft robotics: low cost, low weight, and intrinsic compliance. We exploit morphological computation to simplify control, which allows successful grasping via underactuation. Grasping indeed relies on morphological computation at multiple levels, ranging from the geometry of the actuator which determines the actuator's kinematics, embedded strain sensors to measure curvature, to maximizing contact area and applied force during grasping. Morphological computation reaches its limitations, however, when objects are too bulky to self-align with the gripper or when the state of grasping is of interest. We therefore argue that efficient and reliable grasping also requires not only intrinsic compliance, but also embedded sensing and computation. In particular, we show how embedded sensing can be used to detect successful grasps and vary the force exerted onto an object based on local feedback, which is not possible using morphological computation alone.
A New NOMA Approach for Fair Power Allocation
Venue: arXiv cs.IT 2016
Year: 2016

A non-orthogonal multiple access (NOMA) approach to user signal power allocation called Fair-NOMA is introduced. Fair-NOMA is the application of NOMA in such a way that two mobile users have the opportunity to always achieve at least the information capacity they can achieve by using orthogonal multiple access (OMA), regardless of the user selection criteria, making it suitable for implementation using any current or future scheduling paradigms. Given this condition, the bounds of the power allocation coefficients are derived as functions of the channel gains of the two mobile users. The NOMA power allocation is analyzed for two scheduled users that are selected randomly with i.i.d. channel gains. The capacity improvements made by each user and the sum capacity improvement are derived.
Simple2Complex: Global Optimization by Gradient Descent
Venue: arXiv cs.LG 2016
Year: 2016

A method named simple2complex for modeling and training deep neural networks is proposed. Simple2complex train deep neural networks by smoothly adding more and more layers to the shallow networks, as the learning procedure going on, the network is just like growing. Compared with learning by end2end, simple2complex is with less possibility trapping into local minimal, namely, owning ability for global optimization. Cifar10 is used for verifying the superiority of simple2complex.
Fair Coexistence of Scheduled and Random Access Wireless Networks: Unlicensed LTE/WiFi
Venue: arXiv cs.NI 2016
Year: 2016

We study the fair coexistence of scheduled and random access transmitters sharing the same frequency channel. Interest in coexistence is topical due to the need for emerging unlicensed LTE technologies to coexist fairly with WiFi. However, this interest is not confined to LTE/WiFi as coexistence is likely to become increasingly commonplace in IoT networks and beyond 5G. In this article we show that mixing scheduled and random access incurs and inherent throughput/delay cost, the cost of heterogeneity. We derive the joint proportional fair rate allocation, which casts useful light on current LTE/WiFi discussions. We present experimental results on inter-technology detection and consider the impact of imperfect carrier sensing.
Challenges of Security and Trust of Mobile Devices as Digital Avionics Component
Venue: arXiv cs.CR 2016
Year: 2016

Mobile devices are becoming part of modern digital avionics. Mobile devices can be applied to a range of scenarios, from Electronic Flight Bags to maintenance platforms, in order to manage and configure flight information, configure avionics networks or perform maintenance tasks (including offloading flight logs). It can be argued that recent developments show an increased use of personal mobile devices playing an integral part in the digital avionics industry. In this paper, we look into different proposals for integrating mobile devices with various avionics networks -- either as part of the Bring Your Own Device (BYOD) or Corporate Owned Personally Enabled (COPE) paradigms. Furthermore, we will evaluate the security and trust challenges presented by these devices in their respective domains. This analysis will also include the issues related to communication between the mobile device and the aircraft network via either wired or wireless channels. Finally, the paper puts forward a set of guidelines with regards to the security and trust issues that might be crucial when enabling mobile devices to be part of aircraft networks.